code: def _get_shared_data() -> Any:  # pragma: no cover
      """multiprocessing helper function to extract numpy arrays from
      shared RawArray types used to shared data across process."""
  
      label_counts = _to_np_array(mp_params["label_counts"])
      prune_count_matrix = _to_np_array(
          mp_arr=mp_params["prune_count_matrix"],
          shape=mp_params["pcm_shape"],
      )
      pred_probs = _to_np_array(
          mp_arr=mp_params["pred_probs"],
          dtype="float32",
          shape=mp_params["pred_probs_shape"],
      )
      min_examples_per_class = mp_params["min_examples_per_class"]
      multi_label = mp_params["multi_label"]
      labels = _to_np_array(mp_params["labels"])  # type: ignore
      return (
          labels,
          label_counts,
          prune_count_matrix,
          pred_probs,
          multi_label,
          min_examples_per_class,
      )
  
imports: import numpy
  from sklearn.metrics import confusion_matrix
  import multiprocessing
  import sys
  import warnings
  from typing import Any, Dict, Optional, Tuple, List
  from functools import reduce
  import platform
  from cleanlab.count import calibrate_confident_joint, num_label_issues, _reduce_issues
  from cleanlab.rank import order_label_issues, get_label_quality_scores
  import cleanlab.internal.multilabel_scorer
  from cleanlab.internal.validation import assert_valid_inputs
  from cleanlab.internal.util import value_counts_fill_missing_classes, round_preserving_row_totals, get_num_classes
  from cleanlab.internal.multilabel_utils import stack_complement, get_onehot_num_classes, int2onehot
  from cleanlab.typing import LabelLike
  from cleanlab.multilabel_classification.filter import find_multilabel_issues_per_class
label_desc: Use image node_id 10 for calling a global function with example usage: _get_shared_data() and returns: labels,  label_counts,  prune_count_matrix,  pred_probs,  multi_label,  min_examples_per_class